Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Framework
Abstract
With the increasing integration of frontier large language models (LLMs) into society and the economy, decisions related to their training, deployment, and use have far-reaching implications. These decisions should not be left solely in the hands of frontier LLM developers. LLM users, civil society and policymakers need trustworthy sources of information to steer such decisions for the better. Involving outside actors in the evaluation of these systems - what we term 'external scrutiny' - via red-teaming, auditing, and external researcher access, offers a solution. Though there are encouraging signs of increasing external scrutiny of frontier LLMs, its success is not assured. In this paper, we survey six requirements for effective external scrutiny of frontier AI systems and organize them under the ASPIRE framework: Access, Searching attitude, Proportionality to the risks, Independence, Resources, and Expertise. We then illustrate how external scrutiny might function throughout the AI lifecycle and offer recommendations to policymakers.
Keywords
Cite
@article{arxiv.2311.14711,
title = {Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Framework},
author = {Markus Anderljung and Everett Thornton Smith and Joe O'Brien and Lisa Soder and Benjamin Bucknall and Emma Bluemke and Jonas Schuett and Robert Trager and Lacey Strahm and Rumman Chowdhury},
journal= {arXiv preprint arXiv:2311.14711},
year = {2023}
}
Comments
Accepted to Workshop on Socially Responsible Language Modelling Research (SoLaR) at the 2023 Conference on Neural Information Processing Systems (NeurIPS 2023)